Blinking characteristics of organic fluorophores for blink-based multiplexing

Single-molecule fluorescence experiments have transformed our understanding of complex materials and biological systems. Whether single molecules are used to report on their nano-environment or provide for localization, understanding their blinking dynamics (i.e., stochastic fluctuations in emission intensity under continuous illumination) is paramount. We recently demonstrated another use for blinking dynamics called blink-based multiplexing (BBM), where individual emitters are classified using a single excitation laser based on blinking dynamics, rather than color. This study elucidates the structure-activity relationships governing BBM performance in a series of model rhodamine, BODIPY, and anthraquinone fluorophores that undergo different photo-physical and-chemical processes during blinking. Change point detection and multinomial logistic regression analyses show that BBM can leverage spectral fluctuations, electron and proton transfer kinetics, as well as photostability for molecular classification—even within the context of a shared blinking mechanism. In doing so, we demonstrate two- and three-color BBM with ≥ 93% accuracy using spectrally-overlapped fluorophores.


Additional Tables & Figures:
Figure S1.Ensemble-averaged absorption spectra of all probes (S-2) Table S1.Average CPD-derived blinking statistics of all emitters included in this study (S-3) Figure S2.Distributions of CPD-derived blinking statistics of 5ROX versus R6G (S-4) Figures S3 -S7.Distributions of CPD-derived blinking statistics for R123, R560, RB, PM605, and AZ, respectively.(S-5 to S-9) Table S2.Best-fit parameters for binary classification of 5 Rh dyes using MLR (S-10) Figure S8.Binary classification accuracy of RB/R560 (S-11) Figure S9.Additional MLR analyses as a function of sample size and on mixed samples of known composition (S-12) Table S3.MLE/KS fitting results of the distributions of on-and off-interval durations for all fluorophores in this study (S-13) Table S4.Best-fit parameters for binary classification of 1 Rh versus PM605 or AZ using MLR (S-14) Table S5.MLE/KS fitting results of the on-and off-segment distributions (S-15) Table S6.Ternary BBM-based classification results for 5ROX, R123, R560, R6G, RB, PM605, AZ, and QD (S-16 to S-17) Supplementary References (S-18) S-2 Figure S1.Structures and corresponding ensemble-averaged absorption spectra of the probes examined in this study measured in aqueous or ethanolic solutions: (pink solid) R560, (green dashed) R123, (black dash-dotted) R6G, (red dotted) RB, (blue dashed) 5ROX, (gray solid) AZ, (cyan dash-dotted) QD, and (orange solid) PM605.Primary absorption maxima are observed at 423 nm for AZ, 500 nm for R123 and R560, 526 nm for R6G, 544 nm for PM605, 555 nm for RB, 558 nm for QD, and 578 nm for 5ROX.2][3] The reported fluorescence maxima of the normal 9,10-keto (N) and 1,10-keto tautomer (T) forms of AZ in ethanol are 530 and 615 nm, respectively. 1,2At excitation wavelengths longer than ~480 nm, a primary fluorescence peak centered at 524 nm is observed, consistent with emission from the locally-excited N state of the dye.However, previous ensemble-averaged and single-molecule measurements of AZ at 532nm excitation have reported broad fluorescence from 535 to 700 nm and blinking characteristic of spectral diffusion, which has been attributed to contributions from both the N and T forms of the dye. 1 Shorter excitation wavelengths will further promote ESIPT, which may be useful for BBM.S3, respectively.

S-13
Table S3.MLE/KS fitting results (i.e., fit parameters and goodness-of-fit p-values) for the on-and off-interval durations of the fluorophores included in this study: 5ROX, R123, R560, R6G, RB, PM605, and AZ on glass.Errors represent one standard deviation.The probability that the data matches the hypothetical model is increased as p approaches unity.In general, power laws only represent a small subset of the blinking data as evidenced by relatively large tmin values, which represent the onset time for power-law behavior.a AZ values from Tan et al., which used log-likelihood ratio tests to show that on and off intervals follow lognormal and Weibull distributions, respectively.S1.For classification against AZ, 〈  〉 consistently is second most important, consistent with ESIPT acting to protect AZ from photobleaching and thereby increasing its time-averaged intensity. 1The durations of on and off events are also relatively important.

Figure S2 .
Figure S2.Histograms of (A)   , (B) 〈〉  , (C)   , (D)   , (E)  , , and (F)  ,obtained from CPD analysis of (green, dashed) 95 5ROX molecules and (orange, solid) 148 R6G molecules.The distributions are broad and significantly overlapped, highlighting the need to classify using machine learning rather than individual blinking statistics.The corresponding distributions of on-and off-event durations (Figure3in the manuscript) are even more dispersive, spanning more than four decades in time.

Figures S3 -
Figures S3-S7present histograms of the CPD-derived blinking statistics for all other dye molecules included in this study.The corresponding event duration distributions that are presented in the manuscript (Figure3) or in Tan et al 1 are more dispersive, spanning more than three or four decades in time.The associated distributions for QD were previously reported by Hoy and coworkers.4To facilitate comparison of these distributions between molecules, the bin sizes and ranges of the plots are kept as consistent as possible.The bin sizes in FiguresS3-S7for   , 〈〉  ,   ,   ,  , , and  , correspond to 40 counts 10 ms -1 , 2-4 counts 10 ms -1 , 0.5-1 counts 10 ms -1 , 2, 20, and 4, respectively.

Figure S8 .
Figure S8.(top)RB undergoes stepwise N-dealkylation at 532 nm to produce R560.5,6(bottom) Classification accuracy of RB/R560 is the lowest of all rhodamine trials and plateaus (dashed blue line) at ~60%, even when a classification threshold is applied.Corresponding data retention (red) drops precipitously.BBM does not classify RB/R560, validating the negative control.

Table S2 .
Best-fit parameters (i.e., regression coefficients and intercept, b) of the 10 CPDderived blinking statistics and associated identity to sigmoid functions (Eqn.2) resulting from binary BBM-based classification of 5ROX, R123, R560, R6G, and RB using MLR.Sets in bold represent those that achieve at least 93% accuracy.Although the absolute magnitude of the coefficients cannot be compared across classifications, their relative magnitude within a set report on the statistics governing that particular classification.The largest coefficient within the sets yielding 90% accuracy is highlighted in bold and shows   is relatively significant.Importantly, we performed control experiments on blank glass substrates to show that variations in   are not due to systematic differences in optics, excitation power, or experimental conditions -they relate to the minimum emissive intensity, fluorophore brightness, spectral diffusion, as well as the vibrational stability of the experimental setup, which varies randomly across datasets.When   is excluded as an input predictor for classification, the underlined coefficients have the largest magnitude.The event durations, and in particular 〈 , 〉, are important statistics for rhodamine classification, consistent with variations in the average blinking statistics shown in TableS1.

Table S4 .
1Best-fit parameters resulting from binary BBM-based classification of 1 rhodamine (Rh) (i.e., 5ROX, R123, R560, R6G, or RB) versus PM605 or AZ using MLR.The relative magnitude of the coefficients within a set report on the statistics governing classification.The largest coefficient is presented in bold, which shows   is relatively significant for classification against PM605 or AZ, except for PM605/R6G.The next most important statistics for classification are underlined.In addition to   , the off-segment durations are consistently important for PM605/Rh classification, consistent with differences in the average 〈 , 〉 as shown in Table

Table S5 .
MLE/KS fitting results for the on-and off-segment durations of 5ROX, R123, R560, R6G, RB, and PM605 on glass to lognormal functions.Errors represent one standard deviation.

Table S6 .
Ternary BBM-based classification of 5 Rh (i.e., 5ROX, R123, R560, R6G, RB), PM605, AZ, and QD emitters arranged by (1) classification type and (2) corresponding minimum accuracy.Minimum accuracy corresponds to a default threshold (i.e., PA > 0.33 is classified as A).Subsequent columns contain the classification threshold and corresponding data retention values (i.e., for emitters in class A, B, and C, as well as overall).Sets in bold yield the best BBM performance (i.e., high minimum accuracy and 90% model accuracy with overall data retention >50%).Blank cells indicate the desired accuracy is not achieved with at least 10% data retention.